29 papers
Comment on "Modeling rapid language learning by distilling Bayesian priors into artificial neural networks"
Orr Well, Idan Tarshish, Nur Lan +1
McCoy & Griffiths (2025, henceforth M&G) suggest that a Bayesian prior can be distilled into Artificial Neural Networks (ANNs) through Model-Agnostic Meta-Learning (MAML, Finn et a…
Deformable State Estimation for Autonomous Surgical Tissue Retraction Under Partial Observability
Everest Yang, Skye Thompson, George D. Konidaris
The paper presents a learned estimator that reconstructs the full shape of a deformable tissue mesh from a small set of noisy surface points, enabling more accurate planning for su…
Deform360: A Massive Multi-view Visuotactile Dataset for Deformable World Models
Hongyu Li, Wanjia Fu, Xiaoyan Cong +11
Predicting object dynamics (i.e., world modeling) is a fundamental challenge for robotic manipulation, and modeling deformable objects presents a particularly difficult case due to…
SkillWrapper: Generative Predicate Invention for Task-level Robot Planning
Ziyi Yang, Benned Hedegaard, Ahmed Jaafar +8
Generalizing from individual skill executions to long-horizon tasks is a core challenge in building autonomous robots. A promising direction is learning high-level, symbolic repres…
From Ticks to Flows: Dynamics of Neural Reinforcement Learning in Continuous Environments
Saket Tiwari, Tejas Kotwal, George Konidaris
We present a novel theoretical framework for deep reinforcement learning (RL) in continuous environments by modeling the problem as a continuous-time stochastic process, drawing on…
From Noise to Control: Parameterized Diffusion Policies
Renhao Zhang, Haotian Fu, Mingxi Jia +3
We propose Parameterized Diffusion Policy (PDP), a framework for learning diffusion policies conditioned on low-dimensional, continuous parameters embedded in a learned behavior ma…